An autonomous driving system consumes considerable energy, and it is important to minimize consumption of electricity. A camera, one of components of the autonomous driving system, consumes a lot of electricity, and in some cases, e.g., if no obstacles are nearby since an autonomous vehicle with the autonomous driving system is on a road without much traffic, or if no obstacles are present in a certain direction, the number of required cameras may be small. Operation of every camera in such cases wastes a lot of electricity.
Hence, a technique is required to reduce waste of electricity and to efficiently distribute such resources, but a Convolutional Neural Network, i.e., a CNN, cannot implement this because a ground truth, i.e., GT, cannot be well-defined in such cases, and as a result, a loss cannot be well-defined, making learning processes very difficult. Therefore, a reinforcement learning, one of deep learning mechanisms which does not require the GT, can be utilized. But to realize such a technique through the reinforcement learning, some modules must be actually operated. For example, in case of the autonomous driving, a vehicle must be actually driven to perform the reinforcement learning. However, a car crash is much likely to happen during actual driving of the vehicle.